Logistic regression is a commonly used tool to analyze binary classification problems. However, logisitic regression still faces the limitations of detecting nonlinearities and interactions in data. In this webinar, you will learn more advanced and intuitive machine learning techniques that improve on standard logistic regression in accuracy and other aspects. As an APPLIED example, we will demonstrate using a banking dataset where we will predict future financial stress of a loan applicant in order to determine whether they should be granted a loan. Although the focus is related to finance and loans, the concepts are relevant for anyone who actively uses logistic regression and wishes to improve accuracy and predictor understanding.

Use Battery SHAVE in the Salford Predictive Modeler® software suite to improve your model performance, increase model simplicity, and decrease the number of predictors needed for an accurate model. Using this battery will hep streamline and automate your model for optimal results.

Segmentation (targeting, profiling, classification) is the process of dividing a database into distinct groups of individuals who share common characteristics. This is readily accomplished using modern data mining and machine learning techniques. The methods are easily implemented and work well with large datasets containing nonlinearities, interactions in the data, and a mix of categorical and numerical variables.